Instructions to use qjin/videomae-base-finetuned-kinetics-finetuned-human-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qjin/videomae-base-finetuned-kinetics-finetuned-human-training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="qjin/videomae-base-finetuned-kinetics-finetuned-human-training")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("qjin/videomae-base-finetuned-kinetics-finetuned-human-training") model = AutoModelForVideoClassification.from_pretrained("qjin/videomae-base-finetuned-kinetics-finetuned-human-training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from qjin/videomae-base-finetuned-kinetics-finetuned-human-training: direct link, hf CLI and curl.
- Browser
- Download file 345 MB
-
https://huggingface.co/qjin/videomae-base-finetuned-kinetics-finetuned-human-training/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://qjin/videomae-base-finetuned-kinetics-finetuned-human-training/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/qjin/videomae-base-finetuned-kinetics-finetuned-human-training/resolve/main/pytorch_model.bin
345 MB
- Xet hash:
- 13ae2c98753787a7c1ef698a0939bf0a91870f97a1cd93252e852d1c136c78ed
- Size of remote file:
- 345 MB
- SHA256:
- 2c5012ab1485e5df131baebea3042169e4b803c9fbef15669fec38dd5cf8f2c0
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